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Abstract

A multimodal cost function, based on the local multimodal image descriptors that combine local structure features (orientation, coherency) and modulation/localization information (amplitude, phase, and spatial frequency) of the monogenic wavelet transform, is proposed to estimate the time-varying disparity maps in the space-time stereo framework. The proposed cost function makes use of a constraint of local orientation, phase, and amplitude congruencies with the weighted coefficients, which are adapted to local image features and are insusceptible to level shift, scaling, and rotation and lighting invariance. Experiments on the synthetic and natural stereo sequences show the estimated results are more robust than the intensity-based sum of standard sum of squared difference cost function.

References

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